Paper Citation Record · LEDGER

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks

As of 10 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2502.01680.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.01680 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:11:34.366230Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:19:28.158394Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T22:19:28.592847Z

Reference resolution

27 of 27 outbound references displayed

verified exact2
verified fuzzy21
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1bd2e88e-6f24-4d22-a21d-777273a263b5 · outbound

This paper cites Passenger demand fore- casting in scheduled transportation,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Passenger demand fore- casting in scheduled transportation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.354495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2da96ce3-bc12-4b1e-bcac-2b97fa8861c3 · outbound

This paper cites Bright—drift-aware demand predictions for taxi networks,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Bright—drift-aware demand predictions for taxi networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.328931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6cf8596a-402f-4e7a-9503-a97df8978643 · outbound

This paper cites Deepstcl: A deep spatio-temporal convlstm for travel demand prediction,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Deepstcl: A deep spatio-temporal convlstm for travel demand prediction,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.305793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 702f791c-3149-497e-a0d6-f2fa9f59ad1b · outbound

This paper cites Particular methods of simulta- neous collection of personal mobility research data from several points,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Particular methods of simulta- neous collection of personal mobility research data from several points,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.284313Z

Source-reported events for the cited work

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Observation dca524e8-7d8a-4676-ba52-0bf8236355cd · outbound

This paper cites Integrating household travel survey and social media data to improve the quality of od matrix: A comparative case study,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Integrating household travel survey and social media data to improve the quality of od matrix: A comparative case study,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.262755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 93f21bbf-1cb4-4ce2-89f6-dfbcea2cb783 · outbound

This paper cites Explainable artificial intelligence (xai): Concepts, taxonomies, opportu- nities and challenges toward responsible ai,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Explainable artificial intelligence (xai): Concepts, taxonomies, opportu- nities and challenges toward responsible ai,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:34.195995Z

Source-reported events for the cited work

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Observation 7d0d3d36-86bc-4702-ae4e-c9fb23c61af5 · outbound

This paper cites Neurosymbolic ai: The 3 rd wave,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Neurosymbolic ai: The 3 rd wave,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:34.203588Z

Source-reported events for the cited work

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Observation 11db2e69-c317-4f64-ae9a-f2b9c8c65351 · outbound

This paper cites Gravity model in the korean highway,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Gravity model in the korean highway,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.216188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 461e0366-df0a-4c86-9f9a-bb83b1781f36 · outbound

This paper cites A logistic regression model with a hierarchical random error term for analyzing the utilization of public transport,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks A logistic regression model with a hierarchical random error term for analyzing the utilization of public transport,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.193869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1ff34fcd-2d7f-42f3-9cf6-79a5078d2593 · outbound

This paper cites Regional air mobility flight demand modeling in tennessee state,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Regional air mobility flight demand modeling in tennessee state,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.168448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9a9500c0-1f7c-4fca-b7a5-0cb4d3793dba · outbound

This paper cites Demand modeling for advanced air mobility,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Demand modeling for advanced air mobility,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.145594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 16958f55-9200-40c3-ac0d-c92db4cc380f · outbound

This paper cites A residual spatio-temporal architecture for travel demand forecasting,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks A residual spatio-temporal architecture for travel demand forecasting,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.125753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 039ff631-9dff-41df-8f6a-83c86e27bb4d · outbound

This paper cites Predicting demand for air taxi urban aviation services using machine learning algorithms,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Predicting demand for air taxi urban aviation services using machine learning algorithms,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.102127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0022b0c0-ab51-4629-8e4d-f50f3e682cdf · outbound

This paper cites Improving Air Mobility for Pre-Disaster Planning with Neural Network Accelerated Genetic Algorithm.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Improving Air Mobility for Pre-Disaster Planning with Neural Network Accelerated Genetic Algorithm

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-09T18:11:34.756270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ddbc8339-4d57-446a-9696-2061f5ac10db · outbound

This paper cites Modeling travel mode and timing decisions: Com- parison of artificial neural networks and copula-based joint model,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Modeling travel mode and timing decisions: Com- parison of artificial neural networks and copula-based joint model,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.079802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation de106626-bea3-4eed-a620-175d0d67deb2 · outbound

This paper cites Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.056650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2ee2ea0a-acf8-432c-9812-6e514bce4e10 · outbound

This paper cites an unresolved cited work.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:11:35.034635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 22359391-d41d-487a-9338-8dc9031e2d0a · outbound

This paper cites Analysis of travel mode choice in seoul using an inter- pretable machine learning approach,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Analysis of travel mode choice in seoul using an inter- pretable machine learning approach,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:35.010441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8f161238-70b2-44ff-a1cc-604689845659 · outbound

This paper cites Xgboost: extreme gradient boosting,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Xgboost: extreme gradient boosting,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:34.988654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9b87dfa1-d1e9-4b46-8fbf-777a5a2701c6 · outbound

This paper cites Interpretability of neural networks predictions using accumulated local effects as a model- agnostic method,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Interpretability of neural networks predictions using accumulated local effects as a model- agnostic method,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:34.966869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 816071f5-e445-409a-ae45-9ebb792c0875 · outbound

This paper cites Predicting the travel mode choice with interpretable machine learning techniques: A comparative study,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Predicting the travel mode choice with interpretable machine learning techniques: A comparative study,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:34.925521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cde56ed7-058e-42ea-88f4-93295179f55c · outbound

This paper cites A Unified Approach to Interpreting Model Predictions.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks A Unified Approach to Interpreting Model Predictions

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:34.329940Z

Source-reported events for the cited work

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Observation 5cbc20f8-86e1-4a4c-87af-05961aabf395 · outbound

This paper cites Multiscale dynamic human mobility flow dataset in the u.s. during the covid-19 epidemic,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Multiscale dynamic human mobility flow dataset in the u.s. during the covid-19 epidemic,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:34.880589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 721c478c-5df2-4bfd-ab00-c5c124ebc4ec · outbound

This paper cites Population estimates,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Population estimates,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:34.850681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c540139f-2651-433e-b8a4-b3f93fe4bf0e · outbound

This paper cites Real-time routing with openstreetmap data,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Real-time routing with openstreetmap data,

Reference 25

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T18:11:34.692910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f0d1c203-d6b0-48f1-9d71-26ef501e30be · outbound

This paper cites Tennessee economic and fiscal indicators: March 2020 - july 2021,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks Tennessee economic and fiscal indicators: March 2020 - july 2021,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:34.814083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3facffed-cba4-4245-ae47-9e9c9bff05b2 · outbound

This paper cites County profiles of child well-being in tennessee,.

Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks County profiles of child well-being in tennessee,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:34.782289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 526b7e7f-6dd6-4ff6-abe4-bbd539cbc7f5 · inbound

Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Survey cites this paper.

Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Survey Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:19:28.600575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.